Bottom Line:
Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis.Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression.Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

Background: Dengue virus (DENV) infection causes viral haemorrhagic fever that is characterized by extensive activation of the immune system. The aim of this study is to investigate the kinetics of the transcriptome signature changes during the course of disease and the association of genes in these signatures with clinical parameters.

Methodology/principle findings: Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis. We show that time since onset of disease, but not diagnosis, has a large impact on the blood transcriptome of patients with non-severe dengue. Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression. Network analysis however, indicated that the clinical markers platelet count, fibrinogen, albumin, IV fluid distributed per day and liver enzymes SGOT and SGPT strongly correlate with gene modules that are enriched for genes involved in the immune response. Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

Conclusions/significance: Time since onset of disease associates with the shift in transcriptome signatures from immunity and inflammation to cell cycle and repair mechanisms in patients with non-severe dengue. The strong association of time with blood transcriptome changes hampers both the discovery as well as the potential application of biomarkers in dengue. However, we identified gene expression modules that associate with key clinical parameters of dengue that reflect the systemic activity of disease during the course of infection. The expression level of these gene modules may support earlier detection of disease progression as well as clinical management of dengue.

pntd.0003522.g001: Principle component analysis of all transcriptome snapshots in this study.Icons and colours indicate type and sampling stage of the samples. Timing of samples range from day 0 to day 6 of admission. The day 0 samples have the lightest colour and the day 6 samples the darkest. All probesets were included for this analysis.

Mentions:
To obtain a global overview of the dengue transcriptome profiles, we applied principal component analysis (PCA) (Fig. 1). This non-supervised analysis method finds the ‘optimal point of view’ for observing differences between the samples and depicts this as a distance in a 2-dimensional plot. The first principle component (PC1) accounts for 47% of the variance in the dataset and concurs with time since admission. The second principle component (PC2) accounts for 17% of the variance in gene expression and segregated the dengue samples from the healthy controls; together, PC1 and PC2 account for 64% of gene expression differences in the dataset. PCA did not show any segregation of patients by disease severity according to the 2009 WHO classification. Taken together, PCA demonstrates that time since admission has the highest impact on the dengue transcriptome profiles in our cohort.

pntd.0003522.g001: Principle component analysis of all transcriptome snapshots in this study.Icons and colours indicate type and sampling stage of the samples. Timing of samples range from day 0 to day 6 of admission. The day 0 samples have the lightest colour and the day 6 samples the darkest. All probesets were included for this analysis.

Mentions:
To obtain a global overview of the dengue transcriptome profiles, we applied principal component analysis (PCA) (Fig. 1). This non-supervised analysis method finds the ‘optimal point of view’ for observing differences between the samples and depicts this as a distance in a 2-dimensional plot. The first principle component (PC1) accounts for 47% of the variance in the dataset and concurs with time since admission. The second principle component (PC2) accounts for 17% of the variance in gene expression and segregated the dengue samples from the healthy controls; together, PC1 and PC2 account for 64% of gene expression differences in the dataset. PCA did not show any segregation of patients by disease severity according to the 2009 WHO classification. Taken together, PCA demonstrates that time since admission has the highest impact on the dengue transcriptome profiles in our cohort.

Bottom Line:
Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis.Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression.Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

Background: Dengue virus (DENV) infection causes viral haemorrhagic fever that is characterized by extensive activation of the immune system. The aim of this study is to investigate the kinetics of the transcriptome signature changes during the course of disease and the association of genes in these signatures with clinical parameters.

Methodology/principle findings: Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis. We show that time since onset of disease, but not diagnosis, has a large impact on the blood transcriptome of patients with non-severe dengue. Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression. Network analysis however, indicated that the clinical markers platelet count, fibrinogen, albumin, IV fluid distributed per day and liver enzymes SGOT and SGPT strongly correlate with gene modules that are enriched for genes involved in the immune response. Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection.

Conclusions/significance: Time since onset of disease associates with the shift in transcriptome signatures from immunity and inflammation to cell cycle and repair mechanisms in patients with non-severe dengue. The strong association of time with blood transcriptome changes hampers both the discovery as well as the potential application of biomarkers in dengue. However, we identified gene expression modules that associate with key clinical parameters of dengue that reflect the systemic activity of disease during the course of infection. The expression level of these gene modules may support earlier detection of disease progression as well as clinical management of dengue.